1、附页：一遗传算法源程序：clc;clear;population;%评价目标函数值for uim=1:popsizevector=population(uim,:);obj(uim)=hanshu(hromlength,vector,phen);end%obj%min(obj)clear uim;objmin=min(obj);for sequ=1:popsizeif obj(sequ)=objminopti=population(sequ,:);endendclear sequ;fmax=22000;%=for gen=1:maxgen%选择操作%将求最小值的函数转化为适应度函数for in
2、divi=1:popsizeobj1(indivi)=1/obj(indivi);endclear indivi;%适应度函数累加总合total=0;for indivi=1:popsizetotal=total+obj1(indivi);endclear indivi;%每条染色体被选中的几率for indivi=1:popsizefitness1(indivi)=obj1(indivi)/total;endclear indivi;%各条染色体被选中的范围for indivi=1:popsizefitness(indivi)=0;for j=1:indivifitness(indivi)=
3、fitness(indivi)+fitness1(j);endendclear j;fitness;%选择适应度高的个体for ranseti=1:popsizeran=rand;while (ran1|ranfitness(fet-1) cpoint2=round(rand*hromlength); while (cpoint2=cpoint1) cpoint2=round(rand*hromlength);endif cpoint1cpoint2 tem=cpoint1;cpoint1=cpoint2;cpoint2=tem;endcpoint1;cpoint2;for term=cpoi
4、nt1+1:cpoint2 for ss=1:hromlengthif popcross(int,ss)=popfath(term)tem1=popcross(int,ss);popcross(int,ss)=popcross(int,term);popcross(int,term)=tem1;endendclear tem1;endfor term=cpoint1+1:cpoint2 for ss=1:hromlengthif popcross(int+1,ss)=popmoth(term)tem1=popcross(int+1,ss);popcross(int+1,ss)=popcross
5、(int+1,term);popcross(int+1,term)=tem1;endendclear tem1;endendclear term;endclear randnum;popcross;%变异操作newpop=popcross;for int=1:popsizerandnum=rand;if randnumcpoint12=round(rand*hromlength); cpoint22=round(rand*hromlength); if (cpoint12=0)cpoint12=1;endif (cpoint22=0)cpoint22=1;endwhile (cpoint22=
6、cpoint12) cpoint22=round(rand*hromlength);if cpoint22=0;cpoint22=1;endendtemp=newpop(int,cpoint12);newpop(int,cpoint12)=newpop(int,cpoint22);newpop(int,cpoint22)=temp;endendnewpop;clear cpoint12;clear cpoint22;clear randnum;clear int;for ium=1:popsizevector1=newpop(ium,:);obj1(ium)=hanshu(hromlength
7、,vector1,phen);endclear ium;obj1max=max(obj1);for ar=1:popsizeif obj1(ar)=obj1maxnewpop(ar,:)=opti;endend%遗传操作结束二粒子群算法源程序：%-初始格式化-clear all;clc;format long;%-给定初始化条件-c1=1.4962;%学习因子 1c2=1.4962;%学习因子 2w=0.7298;%惯性权重MaxDT=100;%最大迭代次数D=2;%搜索空间维数（未知数个数）N=40;%初始化群体个体数目eps=10(-6);%设置精度(在已知最小值时候用)%-初始化种群的个
8、体(可以在这里限定位置和速度的范围)-for i=1:Nfor j=1:Dx(i,j)=randn;%随机初始化位置v(i,j)=randn;%随机初始化速度endend%-先计算各个粒子的适应度，并初始化 Pi 和 Pg-for i=1:Np(i)=fitness(x(i,:),D);y(i,:)=x(i,:);endpg=x(1,:);%Pg 为全局最优for i=2:Nif fitness(x(i,:),D)FITNESS(pg,D)pg=x(i,:);endend%-进入主要循环，按照公式依次迭代，直到满足精度要求-for t=1:MaxDTtfor i=1:Nv(i,:)=w*v(i
9、,:)+c1*rand*(y(i,:)-x(i,:)+c2*rand*(pg-x(i,:);x(i,:)=x(i,:)+v(i,:);if fitness(x(i,:),D)p(i)p(i)=fitness(x(i,:),D);y(i,:)=x(i,:);endif p(i)FITNESS(pg,D)pg=y(i,:);endendPbest(t)=fitness(pg,D);end%-进入主要循环，按照公式依次迭代，直到满足精度要求-for t=1:MaxDTfor i=1:Nv(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:)+c2*rand*(pg-x(i,:);
10、x(i,:)=x(i,:)+v(i,:);if fitness(x(i,:),D)p(i)p(i)=fitness(x(i,:),D);y(i,:)=x(i,:);endif p(i)FITNESS(pg,D)pg=y(i,:);endendPbest(t)=fitness(pg,D);end%-最后给出计算结果disp(*)disp(函数的全局最优位置为：)Solution=pgdisp(最后得到的优化极值为：)Result=fitness(pg,D)disp(*)X,Y=meshgrid(-500:2:500);Z=X.*sin(sqrt(X)+Y.*(sin(sqrt(Y);hold oncontour(X,Y,Z)plot(x(:,1),x(:,2),*);hold off
